car license plate character recognition using neural network
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sir again i have one big query...iwant to recognize the license plate charactyer using neural network...but really i m too much confuse that how could i find the link between segmented characters and neural network....means i m unable to find syntax of giving segmented characters as i/p of neural...plese ans me..if any idea...please.........any one help me.....
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Geoff
2012-4-10
0 个投票
I have not done plate recognition, and have only dabbled in neural networks, but I would expect that you need to translate your segmented characters into a vector. That means you first need to standardise the width and height of each segment, which will involve resampling. I would also expect some kind of image normalisation.
Then you can supply each segment as a 1-dimensional vector. The number of pixels in that vector is the number of inputs in your neural network. From memory, the neural network expects inputs along the rows, and each individual training case in the columns. So you would insert each segment-vector into a unique column of your input matrix. The corresponding training output vector, of course, is the character that each segment-vector represents.
Something like that, anyway =)
更多回答（6 个）
Swati
2012-4-10
This one is the code for you,,,of Number plate recognition,,,I have earlier work on this one,,,,in this take a image of car,,then take seperate image of its no plate (for ex,,if no is MP 09-T_456,,,,so take seperate image of M then P,,,then,,,T,,,so on,,,all will b in a single folder n in .bmp format)
may ths one helps you,,,
{I = imread('car.jpg');
I2 = rgb2gray(I);
I4 = im2bw(I2, 0.2);
bw = bwareaopen(I4, 500);
se = strel('disk',15);
bw = imclose(bw,se);
bw = imfill(bw,[1 1]);
[B,L] = bwboundaries(bw,4);
imshow(label2rgb(L, @jet, [.5 .5 .5]))
hold on
for k = 1:length(B)
boundary = B{k};
plot(boundary(:,2),boundary(:,1),'w','LineWidth',2)
end
stats = regionprops(L,'Area','Centroid');
for k = 1:length(B)
boundary = B{k};
delta_sq = diff(boundary).^2;
perimeter = sum(sqrt(sum(delta_sq,2)));
area = stats(k).Area;
metric = 27*area/perimeter^2;
metric_string = sprintf('%2.2f',metric);
if metric >= 0.9 && metric <= 1.1
centroid = stats(k).Centroid;
plot(centroid(1),centroid(2),'ko');
goalboundary = boundary;
s = min(goalboundary, [], 1);
e = max(goalboundary, [], 1);
goal = imcrop(I4,[s(2) s(1) e(2)-s(2) e(1)-s(1)]);
end
text(boundary(1,2)-35,boundary(1,1)+13,...
metric_string,'Color','g',...
'FontSize',14,'FontWeight','bold');
end
goal = ~goal;
goal(256,256) = 0;
figure;
imshow(goal);
w = imread('P.bmp');
w = ~w;
C=real(ifft2(fft2(goal).*fft2(rot90(w,2),256,256)));
thresh = 240;
figure;
imshow(C > thresh);
4 个评论
sneha
2012-4-10
sneha
2012-4-11
Archit Save
2016-1-16
编辑：Archit Save
2016-1-16
i am getting an error...'Undefined function or variable goal'
Image Analyst
2016-1-16
You probably renamed or misspelled a variable somewhere. What variable is given in the call to imshow() just prior to that? If goal weren't a variable, then the imshow() would have thrown an error before the fft line.
sneha
2012-4-14
0 个投票
1 个评论
Derick kundukulam
2014-11-23
did the code work.. even i'm gettin the error C=real(ifft2(fft2(goal).*fft2(rot90(w,2),256,256)));
Image Analyst
2012-4-14
0 个投票
Have you checked here http://iris.usc.edu/Vision-Notes/bibliography/motion-f726.html#License%20Plate%20Recognition,%20Extraction,%20Analysis? Does neural nets seem to be the most common method?
Imtiaz Hussain
2020-2-23
0 个投票
this is my research topic plz can you help me?
1 个评论
Image Analyst
2020-2-23
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